Pandas Profiling Report

Dataset statistics

Number of variables	59
Number of observations	30000
Missing cells	0
Missing cells (%)	0.0%
Duplicate rows	35
Duplicate rows (%)	0.1%
Total size in memory	9.3 MiB
Average record size in memory	326.0 B

Variable types

Numeric	26
Categorical	33

Alerts

Dataset has 35 (0.1%) duplicate rows	Duplicates
`PAY_SEP` is highly correlated with `PAY_AUG` and 6 other fields	High correlation
`PAY_AUG` is highly correlated with `PAY_SEP` and 13 other fields	High correlation
`PAY_JUL` is highly correlated with `PAY_SEP` and 14 other fields	High correlation
`PAY_JUN` is highly correlated with `PAY_SEP` and 16 other fields	High correlation
`PAY_MAY` is highly correlated with `PAY_AUG` and 15 other fields	High correlation
`PAY_APR` is highly correlated with `PAY_AUG` and 14 other fields	High correlation
`BIL_AMT_SEP` is highly correlated with `PAY_AUG` and 14 other fields	High correlation
`BIL_AMT_AUG` is highly correlated with `PAY_AUG` and 16 other fields	High correlation
`BIL_AMT_JUL` is highly correlated with `PAY_AUG` and 17 other fields	High correlation
`BIL_AMT_JUN` is highly correlated with `PAY_JUL` and 19 other fields	High correlation
`BIL_AMT_MAY` is highly correlated with `PAY_JUL` and 18 other fields	High correlation
`BIL_AMT_APR` is highly correlated with `PAY_JUN` and 13 other fields	High correlation
`PAY_AMT_SEP` is highly correlated with `BIL_AMT_SEP` and 5 other fields	High correlation
`PAY_AMT_AUG` is highly correlated with `BIL_AMT_JUL` and 5 other fields	High correlation
`PAY_AMT_JUL` is highly correlated with `BIL_AMT_JUN` and 7 other fields	High correlation
`PAY_AMT_JUN` is highly correlated with `BIL_AMT_JUN` and 6 other fields	High correlation
`PAY_AMT_MAY` is highly correlated with `BIL_AMT_JUN` and 5 other fields	High correlation
`PAY_AMT_APR` is highly correlated with `BIL_AMT_MAY` and 4 other fields	High correlation
`SEX_female` is highly correlated with `SEX_male`	High correlation
`SEX_male` is highly correlated with `SEX_female`	High correlation
`EDUCATION_graduate school` is highly correlated with `EDUCATION_university`	High correlation
`EDUCATION_university` is highly correlated with `EDUCATION_graduate school`	High correlation
`MARRIAGE_married` is highly correlated with `MARRIAGE_single`	High correlation
`MARRIAGE_single` is highly correlated with `MARRIAGE_married`	High correlation
`NO_CONS_APR` is highly correlated with `PAY_JUN` and 9 other fields	High correlation
`PAID_FULL_APR` is highly correlated with `REVOLVING_USE_APR` and 4 other fields	High correlation
`REVOLVING_USE_APR` is highly correlated with `PAY_APR` and 11 other fields	High correlation
`NO_CONS_MAY` is highly correlated with `PAY_JUL` and 10 other fields	High correlation
`PAID_FULL_MAY` is highly correlated with `PAID_FULL_APR` and 4 other fields	High correlation
`REVOLVING_USE_MAY` is highly correlated with `PAY_MAY` and 11 other fields	High correlation
`NO_CONS_JUN` is highly correlated with `PAY_AUG` and 11 other fields	High correlation
`PAID_FULL_JUN` is highly correlated with `PAID_FULL_APR` and 5 other fields	High correlation
`REVOLVING_USE_JUN` is highly correlated with `BIL_AMT_SEP` and 10 other fields	High correlation
`NO_CONS_JUL` is highly correlated with `PAY_AUG` and 9 other fields	High correlation
`PAID_FULL_JUL` is highly correlated with `PAID_FULL_APR` and 5 other fields	High correlation
`REVOLVING_USE_JUL` is highly correlated with `BIL_AMT_SEP` and 9 other fields	High correlation
`NO_CONS_AUG` is highly correlated with `PAY_AUG` and 8 other fields	High correlation
`PAID_FULL_AUG` is highly correlated with `PAY_AUG` and 6 other fields	High correlation
`REVOLVING_USE_AUG` is highly correlated with `BIL_AMT_SEP` and 8 other fields	High correlation
`NO_CONS_SEP` is highly correlated with `PAY_SEP` and 6 other fields	High correlation
`PAID_FULL_SEP` is highly correlated with `PAY_SEP` and 3 other fields	High correlation
`REVOLVING_USE_SEP` is highly correlated with `BIL_AMT_SEP` and 6 other fields	High correlation
`PAY_DELAY_APR` is highly correlated with `PAY_APR` and 2 other fields	High correlation
`PAY_DELAY_MAY` is highly correlated with `PAY_MAY` and 2 other fields	High correlation
`PAY_DELAY_JUN` is highly correlated with `PAY_JUN` and 3 other fields	High correlation
`PAY_DELAY_JUL` is highly correlated with `PAY_JUL` and 2 other fields	High correlation
`PAY_DELAY_AUG` is highly correlated with `PAY_SEP` and 3 other fields	High correlation
`PAY_DELAY_SEP` is highly correlated with `PAY_SEP` and 2 other fields	High correlation
`PAY_SEP` is highly correlated with `PAY_AUG` and 6 other fields	High correlation
`PAY_AUG` is highly correlated with `PAY_SEP` and 9 other fields	High correlation
`PAY_JUL` is highly correlated with `PAY_SEP` and 10 other fields	High correlation
`PAY_JUN` is highly correlated with `PAY_SEP` and 12 other fields	High correlation
`PAY_MAY` is highly correlated with `PAY_SEP` and 11 other fields	High correlation
`PAY_APR` is highly correlated with `PAY_AUG` and 8 other fields	High correlation
`BIL_AMT_SEP` is highly correlated with `BIL_AMT_AUG` and 4 other fields	High correlation
`BIL_AMT_AUG` is highly correlated with `BIL_AMT_SEP` and 4 other fields	High correlation
`BIL_AMT_JUL` is highly correlated with `BIL_AMT_SEP` and 4 other fields	High correlation
`BIL_AMT_JUN` is highly correlated with `BIL_AMT_SEP` and 4 other fields	High correlation
`BIL_AMT_MAY` is highly correlated with `BIL_AMT_SEP` and 4 other fields	High correlation
`BIL_AMT_APR` is highly correlated with `BIL_AMT_SEP` and 4 other fields	High correlation
`SEX_female` is highly correlated with `SEX_male`	High correlation
`SEX_male` is highly correlated with `SEX_female`	High correlation
`EDUCATION_graduate school` is highly correlated with `EDUCATION_university`	High correlation
`EDUCATION_university` is highly correlated with `EDUCATION_graduate school`	High correlation
`MARRIAGE_married` is highly correlated with `MARRIAGE_single`	High correlation
`MARRIAGE_single` is highly correlated with `MARRIAGE_married`	High correlation
`NO_CONS_APR` is highly correlated with `PAY_JUN` and 7 other fields	High correlation
`PAID_FULL_APR` is highly correlated with `REVOLVING_USE_APR` and 4 other fields	High correlation
`REVOLVING_USE_APR` is highly correlated with `PAID_FULL_APR` and 4 other fields	High correlation
`NO_CONS_MAY` is highly correlated with `PAY_JUN` and 7 other fields	High correlation
`PAID_FULL_MAY` is highly correlated with `PAID_FULL_APR` and 4 other fields	High correlation
`REVOLVING_USE_MAY` is highly correlated with `REVOLVING_USE_APR` and 5 other fields	High correlation
`NO_CONS_JUN` is highly correlated with `PAY_JUL` and 8 other fields	High correlation
`PAID_FULL_JUN` is highly correlated with `PAID_FULL_APR` and 5 other fields	High correlation
`REVOLVING_USE_JUN` is highly correlated with `REVOLVING_USE_APR` and 5 other fields	High correlation
`NO_CONS_JUL` is highly correlated with `PAY_AUG` and 8 other fields	High correlation
`PAID_FULL_JUL` is highly correlated with `PAID_FULL_APR` and 5 other fields	High correlation
`REVOLVING_USE_JUL` is highly correlated with `REVOLVING_USE_APR` and 5 other fields	High correlation
`NO_CONS_AUG` is highly correlated with `PAY_AUG` and 6 other fields	High correlation
`PAID_FULL_AUG` is highly correlated with `PAID_FULL_APR` and 5 other fields	High correlation
`REVOLVING_USE_AUG` is highly correlated with `REVOLVING_USE_APR` and 5 other fields	High correlation
`NO_CONS_SEP` is highly correlated with `PAY_SEP` and 5 other fields	High correlation
`PAID_FULL_SEP` is highly correlated with `PAID_FULL_JUN` and 2 other fields	High correlation
`REVOLVING_USE_SEP` is highly correlated with `REVOLVING_USE_MAY` and 3 other fields	High correlation
`PAY_DELAY_APR` is highly correlated with `PAY_JUN` and 4 other fields	High correlation
`PAY_DELAY_MAY` is highly correlated with `PAY_JUN` and 5 other fields	High correlation
`PAY_DELAY_JUN` is highly correlated with `PAY_JUL` and 6 other fields	High correlation
`PAY_DELAY_JUL` is highly correlated with `PAY_AUG` and 6 other fields	High correlation
`PAY_DELAY_AUG` is highly correlated with `PAY_SEP` and 5 other fields	High correlation
`PAY_DELAY_SEP` is highly correlated with `PAY_SEP` and 3 other fields	High correlation
`PAY_SEP` is highly correlated with `PAY_AUG` and 3 other fields	High correlation
`PAY_AUG` is highly correlated with `PAY_SEP` and 7 other fields	High correlation
`PAY_JUL` is highly correlated with `PAY_SEP` and 8 other fields	High correlation
`PAY_JUN` is highly correlated with `PAY_AUG` and 9 other fields	High correlation
`PAY_MAY` is highly correlated with `PAY_AUG` and 9 other fields	High correlation
`PAY_APR` is highly correlated with `PAY_AUG` and 10 other fields	High correlation
`BIL_AMT_SEP` is highly correlated with `BIL_AMT_AUG` and 4 other fields	High correlation
`BIL_AMT_AUG` is highly correlated with `BIL_AMT_SEP` and 6 other fields	High correlation
`BIL_AMT_JUL` is highly correlated with `BIL_AMT_SEP` and 6 other fields	High correlation
`BIL_AMT_JUN` is highly correlated with `PAY_MAY` and 5 other fields	High correlation
`BIL_AMT_MAY` is highly correlated with `PAY_APR` and 6 other fields	High correlation
`BIL_AMT_APR` is highly correlated with `PAY_APR` and 6 other fields	High correlation
`PAY_AMT_SEP` is highly correlated with `BIL_AMT_AUG`	High correlation
`PAY_AMT_AUG` is highly correlated with `BIL_AMT_JUL`	High correlation
`PAY_AMT_JUN` is highly correlated with `BIL_AMT_MAY`	High correlation
`PAY_AMT_MAY` is highly correlated with `BIL_AMT_APR`	High correlation
`SEX_female` is highly correlated with `SEX_male`	High correlation
`SEX_male` is highly correlated with `SEX_female`	High correlation
`EDUCATION_graduate school` is highly correlated with `EDUCATION_university`	High correlation
`EDUCATION_university` is highly correlated with `EDUCATION_graduate school`	High correlation
`MARRIAGE_married` is highly correlated with `MARRIAGE_single`	High correlation
`MARRIAGE_single` is highly correlated with `MARRIAGE_married`	High correlation
`NO_CONS_APR` is highly correlated with `PAY_JUN` and 7 other fields	High correlation
`PAID_FULL_APR` is highly correlated with `REVOLVING_USE_APR` and 4 other fields	High correlation
`REVOLVING_USE_APR` is highly correlated with `PAID_FULL_APR` and 4 other fields	High correlation
`NO_CONS_MAY` is highly correlated with `PAY_JUN` and 7 other fields	High correlation
`PAID_FULL_MAY` is highly correlated with `PAID_FULL_APR` and 4 other fields	High correlation
`REVOLVING_USE_MAY` is highly correlated with `REVOLVING_USE_APR` and 5 other fields	High correlation
`NO_CONS_JUN` is highly correlated with `PAY_JUL` and 8 other fields	High correlation
`PAID_FULL_JUN` is highly correlated with `PAID_FULL_APR` and 5 other fields	High correlation
`REVOLVING_USE_JUN` is highly correlated with `BIL_AMT_JUL` and 6 other fields	High correlation
`NO_CONS_JUL` is highly correlated with `PAY_AUG` and 9 other fields	High correlation
`PAID_FULL_JUL` is highly correlated with `PAID_FULL_APR` and 5 other fields	High correlation
`REVOLVING_USE_JUL` is highly correlated with `BIL_AMT_AUG` and 6 other fields	High correlation
`NO_CONS_AUG` is highly correlated with `PAY_AUG` and 7 other fields	High correlation
`PAID_FULL_AUG` is highly correlated with `PAID_FULL_APR` and 5 other fields	High correlation
`REVOLVING_USE_AUG` is highly correlated with `REVOLVING_USE_APR` and 5 other fields	High correlation
`NO_CONS_SEP` is highly correlated with `NO_CONS_APR` and 4 other fields	High correlation
`PAID_FULL_SEP` is highly correlated with `PAY_SEP` and 3 other fields	High correlation
`REVOLVING_USE_SEP` is highly correlated with `REVOLVING_USE_MAY` and 4 other fields	High correlation
`PAY_DELAY_APR` is highly correlated with `PAY_APR` and 2 other fields	High correlation
`PAY_DELAY_MAY` is highly correlated with `PAY_MAY` and 2 other fields	High correlation
`PAY_DELAY_JUN` is highly correlated with `PAY_JUN` and 3 other fields	High correlation
`PAY_DELAY_JUL` is highly correlated with `PAY_JUL` and 2 other fields	High correlation
`PAY_DELAY_AUG` is highly correlated with `PAY_AUG` and 2 other fields	High correlation
`PAY_DELAY_SEP` is highly correlated with `PAY_SEP` and 2 other fields	High correlation
`NO_CONS_SEP` is highly correlated with `NO_CONS_JUL` and 4 other fields	High correlation
`REVOLVING_USE_APR` is highly correlated with `REVOLVING_USE_AUG` and 4 other fields	High correlation
`PAID_FULL_JUN` is highly correlated with `PAID_FULL_SEP` and 5 other fields	High correlation
`REVOLVING_USE_AUG` is highly correlated with `REVOLVING_USE_APR` and 5 other fields	High correlation
`PAID_FULL_SEP` is highly correlated with `PAID_FULL_JUN` and 2 other fields	High correlation
`REVOLVING_USE_SEP` is highly correlated with `REVOLVING_USE_AUG` and 3 other fields	High correlation
`EDUCATION_university` is highly correlated with `EDUCATION_graduate school` and 1 other fields	High correlation
`REVOLVING_USE_MAY` is highly correlated with `REVOLVING_USE_APR` and 5 other fields	High correlation
`MARRIAGE_divorce` is highly correlated with `MARRIAGE_V`	High correlation
`MARRIAGE_single` is highly correlated with `MARRIAGE_married` and 1 other fields	High correlation
`EDUCATION_graduate school` is highly correlated with `EDUCATION_university` and 1 other fields	High correlation
`DEFAULT` is highly correlated with `DEFAULT_NUM`	High correlation
`SEX_male` is highly correlated with `SEX_V` and 1 other fields	High correlation
`SEX_V` is highly correlated with `SEX_male` and 1 other fields	High correlation
`PAID_FULL_MAY` is highly correlated with `PAID_FULL_JUN` and 4 other fields	High correlation
`REVOLVING_USE_JUL` is highly correlated with `REVOLVING_USE_APR` and 5 other fields	High correlation
`MARRIAGE_married` is highly correlated with `MARRIAGE_single` and 1 other fields	High correlation
`NO_CONS_JUL` is highly correlated with `NO_CONS_SEP` and 4 other fields	High correlation
`NO_CONS_APR` is highly correlated with `NO_CONS_SEP` and 4 other fields	High correlation
`NO_CONS_AUG` is highly correlated with `NO_CONS_SEP` and 4 other fields	High correlation
`EDUCATION_other` is highly correlated with `EDUCATION_V`	High correlation
`PAID_FULL_AUG` is highly correlated with `PAID_FULL_JUN` and 5 other fields	High correlation
`REVOLVING_USE_JUN` is highly correlated with `REVOLVING_USE_APR` and 5 other fields	High correlation
`MARRIAGE_V` is highly correlated with `MARRIAGE_divorce` and 3 other fields	High correlation
`EDUCATION_high school` is highly correlated with `EDUCATION_V`	High correlation
`PAID_FULL_JUL` is highly correlated with `PAID_FULL_JUN` and 5 other fields	High correlation
`NO_CONS_MAY` is highly correlated with `NO_CONS_SEP` and 4 other fields	High correlation
`EDUCATION_V` is highly correlated with `EDUCATION_university` and 3 other fields	High correlation
`PAID_FULL_APR` is highly correlated with `REVOLVING_USE_APR` and 4 other fields	High correlation
`MARRIAGE_others` is highly correlated with `MARRIAGE_V`	High correlation
`SEX_female` is highly correlated with `SEX_male` and 1 other fields	High correlation
`DEFAULT_NUM` is highly correlated with `DEFAULT`	High correlation
`NO_CONS_JUN` is highly correlated with `NO_CONS_SEP` and 4 other fields	High correlation
`LIMIT_BAL` is highly correlated with `BIL_AMT_SEP` and 5 other fields	High correlation
`AGE` is highly correlated with `MARRIAGE_married` and 1 other fields	High correlation
`PAY_SEP` is highly correlated with `PAY_AUG` and 28 other fields	High correlation
`PAY_AUG` is highly correlated with `PAY_SEP` and 26 other fields	High correlation
`PAY_JUL` is highly correlated with `PAY_SEP` and 28 other fields	High correlation
`PAY_JUN` is highly correlated with `PAY_SEP` and 28 other fields	High correlation
`PAY_MAY` is highly correlated with `PAY_SEP` and 28 other fields	High correlation
`PAY_APR` is highly correlated with `PAY_SEP` and 28 other fields	High correlation
`BIL_AMT_SEP` is highly correlated with `LIMIT_BAL` and 6 other fields	High correlation
`BIL_AMT_AUG` is highly correlated with `LIMIT_BAL` and 6 other fields	High correlation
`BIL_AMT_JUL` is highly correlated with `BIL_AMT_SEP` and 6 other fields	High correlation
`BIL_AMT_JUN` is highly correlated with `LIMIT_BAL` and 8 other fields	High correlation
`BIL_AMT_MAY` is highly correlated with `LIMIT_BAL` and 8 other fields	High correlation
`BIL_AMT_APR` is highly correlated with `LIMIT_BAL` and 6 other fields	High correlation
`PAY_AMT_SEP` is highly correlated with `PAY_AMT_AUG` and 2 other fields	High correlation
`PAY_AMT_AUG` is highly correlated with `BIL_AMT_JUL` and 3 other fields	High correlation
`PAY_AMT_JUL` is highly correlated with `LIMIT_BAL` and 8 other fields	High correlation
`PAY_AMT_JUN` is highly correlated with `PAY_AMT_SEP` and 1 other fields	High correlation
`PAY_AMT_MAY` is highly correlated with `BIL_AMT_JUL` and 1 other fields	High correlation
`DEFAULT` is highly correlated with `PAY_SEP` and 1 other fields	High correlation
`SEX_V` is highly correlated with `SEX_female` and 1 other fields	High correlation
`EDUCATION_V` is highly correlated with `EDUCATION_graduate school` and 3 other fields	High correlation
`MARRIAGE_V` is highly correlated with `MARRIAGE_divorce` and 3 other fields	High correlation
`SEX_female` is highly correlated with `SEX_V` and 1 other fields	High correlation
`SEX_male` is highly correlated with `SEX_V` and 1 other fields	High correlation
`EDUCATION_graduate school` is highly correlated with `EDUCATION_V` and 1 other fields	High correlation
`EDUCATION_high school` is highly correlated with `EDUCATION_V` and 1 other fields	High correlation
`EDUCATION_other` is highly correlated with `EDUCATION_V`	High correlation
`EDUCATION_university` is highly correlated with `EDUCATION_V` and 2 other fields	High correlation
`MARRIAGE_divorce` is highly correlated with `MARRIAGE_V`	High correlation
`MARRIAGE_married` is highly correlated with `AGE` and 2 other fields	High correlation
`MARRIAGE_others` is highly correlated with `MARRIAGE_V`	High correlation
`MARRIAGE_single` is highly correlated with `AGE` and 2 other fields	High correlation
`DEFAULT_NUM` is highly correlated with `PAY_SEP` and 1 other fields	High correlation
`NO_CONS_APR` is highly correlated with `PAY_SEP` and 14 other fields	High correlation
`PAID_FULL_APR` is highly correlated with `PAY_SEP` and 12 other fields	High correlation
`REVOLVING_USE_APR` is highly correlated with `PAY_SEP` and 18 other fields	High correlation
`NO_CONS_MAY` is highly correlated with `PAY_SEP` and 14 other fields	High correlation
`PAID_FULL_MAY` is highly correlated with `PAY_SEP` and 14 other fields	High correlation
`REVOLVING_USE_MAY` is highly correlated with `PAY_SEP` and 20 other fields	High correlation
`NO_CONS_JUN` is highly correlated with `PAY_SEP` and 15 other fields	High correlation
`PAID_FULL_JUN` is highly correlated with `PAY_SEP` and 14 other fields	High correlation
`REVOLVING_USE_JUN` is highly correlated with `PAY_SEP` and 19 other fields	High correlation
`NO_CONS_JUL` is highly correlated with `PAY_SEP` and 16 other fields	High correlation
`PAID_FULL_JUL` is highly correlated with `PAY_SEP` and 14 other fields	High correlation
`REVOLVING_USE_JUL` is highly correlated with `PAY_SEP` and 20 other fields	High correlation
`NO_CONS_AUG` is highly correlated with `PAY_SEP` and 16 other fields	High correlation
`PAID_FULL_AUG` is highly correlated with `PAY_SEP` and 14 other fields	High correlation
`REVOLVING_USE_AUG` is highly correlated with `PAY_SEP` and 18 other fields	High correlation
`NO_CONS_SEP` is highly correlated with `PAY_SEP` and 11 other fields	High correlation
`PAID_FULL_SEP` is highly correlated with `PAY_SEP` and 11 other fields	High correlation
`REVOLVING_USE_SEP` is highly correlated with `PAY_SEP` and 16 other fields	High correlation
`PAY_DELAY_APR` is highly correlated with `PAY_JUL` and 6 other fields	High correlation
`PAY_DELAY_MAY` is highly correlated with `PAY_JUL` and 8 other fields	High correlation
`PAY_DELAY_JUN` is highly correlated with `PAY_SEP` and 10 other fields	High correlation
`PAY_DELAY_JUL` is highly correlated with `PAY_SEP` and 10 other fields	High correlation
`PAY_DELAY_AUG` is highly correlated with `PAY_SEP` and 9 other fields	High correlation
`PAY_DELAY_SEP` is highly correlated with `PAY_SEP` and 9 other fields	High correlation
`PAY_AMT_AUG` is highly skewed (γ1 = 30.45381745)	Skewed
`PAY_SEP` has 14737 (49.1%) zeros	Zeros
`PAY_AUG` has 15730 (52.4%) zeros	Zeros
`PAY_JUL` has 15764 (52.5%) zeros	Zeros
`PAY_JUN` has 16455 (54.9%) zeros	Zeros
`PAY_MAY` has 16947 (56.5%) zeros	Zeros
`PAY_APR` has 16286 (54.3%) zeros	Zeros
`BIL_AMT_SEP` has 2008 (6.7%) zeros	Zeros
`BIL_AMT_AUG` has 2506 (8.4%) zeros	Zeros
`BIL_AMT_JUL` has 2870 (9.6%) zeros	Zeros
`BIL_AMT_JUN` has 3195 (10.7%) zeros	Zeros
`BIL_AMT_MAY` has 3506 (11.7%) zeros	Zeros
`BIL_AMT_APR` has 4020 (13.4%) zeros	Zeros
`PAY_AMT_SEP` has 5249 (17.5%) zeros	Zeros
`PAY_AMT_AUG` has 5396 (18.0%) zeros	Zeros
`PAY_AMT_JUL` has 5968 (19.9%) zeros	Zeros
`PAY_AMT_JUN` has 6408 (21.4%) zeros	Zeros
`PAY_AMT_MAY` has 6703 (22.3%) zeros	Zeros
`PAY_AMT_APR` has 7173 (23.9%) zeros	Zeros
`PAY_DELAY_APR` has 26921 (89.7%) zeros	Zeros
`PAY_DELAY_MAY` has 27032 (90.1%) zeros	Zeros
`PAY_DELAY_JUN` has 26490 (88.3%) zeros	Zeros
`PAY_DELAY_JUL` has 25787 (86.0%) zeros	Zeros
`PAY_DELAY_AUG` has 25562 (85.2%) zeros	Zeros
`PAY_DELAY_SEP` has 23182 (77.3%) zeros	Zeros

Reproduction

Analysis started	2021-10-08 01:28:15.537026
Analysis finished	2021-10-08 01:30:17.535031
Duration	2 minutes and 2 seconds
Software version	pandas-profiling v3.1.0
Download configuration	config.json

LIMIT_BAL
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	81
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	167484.3227

Minimum	10000
Maximum	1000000
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	234.5 KiB

Quantile statistics

Minimum	10000
5-th percentile	20000
Q1	50000
median	140000
Q3	240000
95-th percentile	430000
Maximum	1000000
Range	990000
Interquartile range (IQR)	190000

Descriptive statistics

Standard deviation	129747.6616
Coefficient of variation (CV)	0.7746854124
Kurtosis	0.5362628964
Mean	167484.3227
Median Absolute Deviation (MAD)	90000
Skewness	0.9928669605
Sum	5024529680
Variance	1.683445568 × 10¹⁰
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
50000	3365	11.2%
20000	1976	6.6%
30000	1610	5.4%
80000	1567	5.2%
200000	1528	5.1%
150000	1110	3.7%
100000	1048	3.5%
180000	995	3.3%
360000	881	2.9%
60000	825	2.8%
Other values (71)	15095	50.3%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
10000	493	1.6%
16000	2	< 0.1%
20000	1976	6.6%
30000	1610	5.4%
40000	230	0.8%
50000	3365	11.2%
60000	825	2.8%
70000	731	2.4%
80000	1567	5.2%
90000	651	2.2%

Value	Count	Frequency (%)
1000000	1	< 0.1%
800000	2	< 0.1%
780000	2	< 0.1%
760000	1	< 0.1%
750000	4	< 0.1%
740000	2	< 0.1%
730000	2	< 0.1%
720000	3	< 0.1%
710000	6	< 0.1%
700000	8	< 0.1%

AGE
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	56
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	35.4855

Minimum	21
Maximum	79
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	234.5 KiB

Quantile statistics

Minimum	21
5-th percentile	23
Q1	28
median	34
Q3	41
95-th percentile	53
Maximum	79
Range	58
Interquartile range (IQR)	13

Descriptive statistics

Standard deviation	9.217904068
Coefficient of variation (CV)	0.2597653709
Kurtosis	0.04430337824
Mean	35.4855
Median Absolute Deviation (MAD)	6
Skewness	0.7322458688
Sum	1064565
Variance	84.96975541
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
29	1605	5.3%
27	1477	4.9%
28	1409	4.7%
30	1395	4.7%
26	1256	4.2%
31	1217	4.1%
25	1186	4.0%
34	1162	3.9%
32	1158	3.9%
33	1146	3.8%
Other values (46)	16989	56.6%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
21	67	0.2%
22	560	1.9%
23	931	3.1%
24	1127	3.8%
25	1186	4.0%
26	1256	4.2%
27	1477	4.9%
28	1409	4.7%
29	1605	5.3%
30	1395	4.7%

Value	Count	Frequency (%)
79	1	< 0.1%
75	3	< 0.1%
74	1	< 0.1%
73	4	< 0.1%
72	3	< 0.1%
71	3	< 0.1%
70	10	< 0.1%
69	15	0.1%
68	5	< 0.1%
67	16	0.1%

PAY_SEP
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	11
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	-0.0167

Minimum	-2
Maximum	8
Zeros	14737
Zeros (%)	49.1%
Negative	8445
Negative (%)	28.1%
Memory size	234.5 KiB

Quantile statistics

Minimum	-2
5-th percentile	-2
Q1	-1
median	0
Q3	0
95-th percentile	2
Maximum	8
Range	10
Interquartile range (IQR)	1

Descriptive statistics

Standard deviation	1.123801528
Coefficient of variation (CV)	-67.29350467
Kurtosis	2.720715042
Mean	-0.0167
Median Absolute Deviation (MAD)	1
Skewness	0.7319749269
Sum	-501
Variance	1.262929874
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=11)

Value	Count	Frequency (%)
0	14737	49.1%
-1	5686	19.0%
1	3688	12.3%
-2	2759	9.2%
2	2667	8.9%
3	322	1.1%
4	76	0.3%
5	26	0.1%
8	19	0.1%
6	11	< 0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
-2	2759	9.2%
-1	5686	19.0%
0	14737	49.1%
1	3688	12.3%
2	2667	8.9%
3	322	1.1%
4	76	0.3%
5	26	0.1%
6	11	< 0.1%
7	9	< 0.1%

Value	Count	Frequency (%)
8	19	0.1%
7	9	< 0.1%
6	11	< 0.1%
5	26	0.1%
4	76	0.3%
3	322	1.1%
2	2667	8.9%
1	3688	12.3%
0	14737	49.1%
-1	5686	19.0%

PAY_AUG
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	11
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	-0.1337666667

Minimum	-2
Maximum	8
Zeros	15730
Zeros (%)	52.4%
Negative	9832
Negative (%)	32.8%
Memory size	234.5 KiB

Quantile statistics

Minimum	-2
5-th percentile	-2
Q1	-1
median	0
Q3	0
95-th percentile	2
Maximum	8
Range	10
Interquartile range (IQR)	1

Descriptive statistics

Standard deviation	1.197185973
Coefficient of variation (CV)	-8.949807922
Kurtosis	1.57041773
Mean	-0.1337666667
Median Absolute Deviation (MAD)	0
Skewness	0.7905650222
Sum	-4013
Variance	1.433254254
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=11)

Value	Count	Frequency (%)
0	15730	52.4%
-1	6050	20.2%
2	3927	13.1%
-2	3782	12.6%
3	326	1.1%
4	99	0.3%
1	28	0.1%
5	25	0.1%
7	20	0.1%
6	12	< 0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
-2	3782	12.6%
-1	6050	20.2%
0	15730	52.4%
1	28	0.1%
2	3927	13.1%
3	326	1.1%
4	99	0.3%
5	25	0.1%
6	12	< 0.1%
7	20	0.1%

Value	Count	Frequency (%)
8	1	< 0.1%
7	20	0.1%
6	12	< 0.1%
5	25	0.1%
4	99	0.3%
3	326	1.1%
2	3927	13.1%
1	28	0.1%
0	15730	52.4%
-1	6050	20.2%

PAY_JUL
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	11
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	-0.1662

Minimum	-2
Maximum	8
Zeros	15764
Zeros (%)	52.5%
Negative	10023
Negative (%)	33.4%
Memory size	234.5 KiB

Quantile statistics

Minimum	-2
5-th percentile	-2
Q1	-1
median	0
Q3	0
95-th percentile	2
Maximum	8
Range	10
Interquartile range (IQR)	1

Descriptive statistics

Standard deviation	1.196867568
Coefficient of variation (CV)	-7.201369245
Kurtosis	2.084435875
Mean	-0.1662
Median Absolute Deviation (MAD)	0
Skewness	0.8406818269
Sum	-4986
Variance	1.432491976
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=11)

Value	Count	Frequency (%)
0	15764	52.5%
-1	5938	19.8%
-2	4085	13.6%
2	3819	12.7%
3	240	0.8%
4	76	0.3%
7	27	0.1%
6	23	0.1%
5	21	0.1%
1	4	< 0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
-2	4085	13.6%
-1	5938	19.8%
0	15764	52.5%
1	4	< 0.1%
2	3819	12.7%
3	240	0.8%
4	76	0.3%
5	21	0.1%
6	23	0.1%
7	27	0.1%

Value	Count	Frequency (%)
8	3	< 0.1%
7	27	0.1%
6	23	0.1%
5	21	0.1%
4	76	0.3%
3	240	0.8%
2	3819	12.7%
1	4	< 0.1%
0	15764	52.5%
-1	5938	19.8%

PAY_JUN
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	11
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	-0.2206666667

Minimum	-2
Maximum	8
Zeros	16455
Zeros (%)	54.9%
Negative	10035
Negative (%)	33.5%
Memory size	234.5 KiB

Quantile statistics

Minimum	-2
5-th percentile	-2
Q1	-1
median	0
Q3	0
95-th percentile	2
Maximum	8
Range	10
Interquartile range (IQR)	1

Descriptive statistics

Standard deviation	1.169138622
Coefficient of variation (CV)	-5.29821128
Kurtosis	3.496983496
Mean	-0.2206666667
Median Absolute Deviation (MAD)	0
Skewness	0.9996294133
Sum	-6620
Variance	1.366885118
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=11)

Value	Count	Frequency (%)
0	16455	54.9%
-1	5687	19.0%
-2	4348	14.5%
2	3159	10.5%
3	180	0.6%
4	69	0.2%
7	58	0.2%
5	35	0.1%
6	5	< 0.1%
1	2	< 0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
-2	4348	14.5%
-1	5687	19.0%
0	16455	54.9%
1	2	< 0.1%
2	3159	10.5%
3	180	0.6%
4	69	0.2%
5	35	0.1%
6	5	< 0.1%
7	58	0.2%

Value	Count	Frequency (%)
8	2	< 0.1%
7	58	0.2%
6	5	< 0.1%
5	35	0.1%
4	69	0.2%
3	180	0.6%
2	3159	10.5%
1	2	< 0.1%
0	16455	54.9%
-1	5687	19.0%

PAY_MAY
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	10
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	-0.2662

Minimum	-2
Maximum	8
Zeros	16947
Zeros (%)	56.5%
Negative	10085
Negative (%)	33.6%
Memory size	234.5 KiB

Quantile statistics

Minimum	-2
5-th percentile	-2
Q1	-1
median	0
Q3	0
95-th percentile	2
Maximum	8
Range	10
Interquartile range (IQR)	1

Descriptive statistics

Standard deviation	1.133187406
Coefficient of variation (CV)	-4.256902352
Kurtosis	3.989748144
Mean	-0.2662
Median Absolute Deviation (MAD)	0
Skewness	1.008197025
Sum	-7986
Variance	1.284113697
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=10)

Value	Count	Frequency (%)
0	16947	56.5%
-1	5539	18.5%
-2	4546	15.2%
2	2626	8.8%
3	178	0.6%
4	84	0.3%
7	58	0.2%
5	17	0.1%
6	4	< 0.1%
8	1	< 0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
-2	4546	15.2%
-1	5539	18.5%
0	16947	56.5%
2	2626	8.8%
3	178	0.6%
4	84	0.3%
5	17	0.1%
6	4	< 0.1%
7	58	0.2%
8	1	< 0.1%

Value	Count	Frequency (%)
8	1	< 0.1%
7	58	0.2%
6	4	< 0.1%
5	17	0.1%
4	84	0.3%
3	178	0.6%
2	2626	8.8%
0	16947	56.5%
-1	5539	18.5%
-2	4546	15.2%

PAY_APR
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	10
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	-0.2911

Minimum	-2
Maximum	8
Zeros	16286
Zeros (%)	54.3%
Negative	10635
Negative (%)	35.4%
Memory size	234.5 KiB

Quantile statistics

Minimum	-2
5-th percentile	-2
Q1	-1
median	0
Q3	0
95-th percentile	2
Maximum	8
Range	10
Interquartile range (IQR)	1

Descriptive statistics

Standard deviation	1.149987626
Coefficient of variation (CV)	-3.950489954
Kurtosis	3.42653413
Mean	-0.2911
Median Absolute Deviation (MAD)	0
Skewness	0.9480293916
Sum	-8733
Variance	1.322471539
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=10)

Value	Count	Frequency (%)
0	16286	54.3%
-1	5740	19.1%
-2	4895	16.3%
2	2766	9.2%
3	184	0.6%
4	49	0.2%
7	46	0.2%
6	19	0.1%
5	13	< 0.1%
8	2	< 0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
-2	4895	16.3%
-1	5740	19.1%
0	16286	54.3%
2	2766	9.2%
3	184	0.6%
4	49	0.2%
5	13	< 0.1%
6	19	0.1%
7	46	0.2%
8	2	< 0.1%

Value	Count	Frequency (%)
8	2	< 0.1%
7	46	0.2%
6	19	0.1%
5	13	< 0.1%
4	49	0.2%
3	184	0.6%
2	2766	9.2%
0	16286	54.3%
-1	5740	19.1%
-2	4895	16.3%

BIL_AMT_SEP
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	22723
Distinct (%)	75.7%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	51223.3309

Minimum	-165580
Maximum	964511
Zeros	2008
Zeros (%)	6.7%
Negative	590
Negative (%)	2.0%
Memory size	234.5 KiB

Quantile statistics

Minimum	-165580
5-th percentile	0
Q1	3558.75
median	22381.5
Q3	67091
95-th percentile	201203.05
Maximum	964511
Range	1130091
Interquartile range (IQR)	63532.25

Descriptive statistics

Standard deviation	73635.86058
Coefficient of variation (CV)	1.437545339
Kurtosis	9.806289341
Mean	51223.3309
Median Absolute Deviation (MAD)	21800.5
Skewness	2.663861022
Sum	1536699927
Variance	5422239963
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	2008	6.7%
390	244	0.8%
780	76	0.3%
326	72	0.2%
316	63	0.2%
2500	59	0.2%
396	49	0.2%
2400	39	0.1%
416	29	0.1%
500	25	0.1%
Other values (22713)	27336	91.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
-165580	1	< 0.1%
-154973	1	< 0.1%
-15308	1	< 0.1%
-14386	1	< 0.1%
-11545	1	< 0.1%
-10682	1	< 0.1%
-9802	1	< 0.1%
-9095	1	< 0.1%
-8187	1	< 0.1%
-7438	1	< 0.1%

Value	Count	Frequency (%)
964511	1	< 0.1%
746814	1	< 0.1%
653062	1	< 0.1%
630458	1	< 0.1%
626648	1	< 0.1%
621749	1	< 0.1%
613860	1	< 0.1%
610723	1	< 0.1%
608594	1	< 0.1%
604019	1	< 0.1%

BIL_AMT_AUG
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	22346
Distinct (%)	74.5%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	49179.07517

Minimum	-69777
Maximum	983931
Zeros	2506
Zeros (%)	8.4%
Negative	669
Negative (%)	2.2%
Memory size	234.5 KiB

Quantile statistics

Minimum	-69777
5-th percentile	0
Q1	2984.75
median	21200
Q3	64006.25
95-th percentile	194792.2
Maximum	983931
Range	1053708
Interquartile range (IQR)	61021.5

Descriptive statistics

Standard deviation	71173.76878
Coefficient of variation (CV)	1.447236829
Kurtosis	10.30294592
Mean	49179.07517
Median Absolute Deviation (MAD)	20810
Skewness	2.705220853
Sum	1475372255
Variance	5065705363
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	2506	8.4%
390	231	0.8%
326	75	0.2%
780	75	0.2%
316	72	0.2%
396	51	0.2%
2500	51	0.2%
2400	42	0.1%
-200	29	0.1%
416	28	0.1%
Other values (22336)	26840	89.5%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
-69777	1	< 0.1%
-67526	1	< 0.1%
-33350	1	< 0.1%
-30000	1	< 0.1%
-26214	1	< 0.1%
-24704	1	< 0.1%
-24702	1	< 0.1%
-22960	1	< 0.1%
-18618	1	< 0.1%
-18088	1	< 0.1%

Value	Count	Frequency (%)
983931	1	< 0.1%
743970	1	< 0.1%
671563	1	< 0.1%
646770	1	< 0.1%
624475	1	< 0.1%
605943	1	< 0.1%
597793	1	< 0.1%
586825	1	< 0.1%
581775	1	< 0.1%
577681	1	< 0.1%

BIL_AMT_JUL
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	22026
Distinct (%)	73.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	47013.1548

Minimum	-157264
Maximum	1664089
Zeros	2870
Zeros (%)	9.6%
Negative	655
Negative (%)	2.2%
Memory size	234.5 KiB

Quantile statistics

Minimum	-157264
5-th percentile	0
Q1	2666.25
median	20088.5
Q3	60164.75
95-th percentile	187821.05
Maximum	1664089
Range	1821353
Interquartile range (IQR)	57498.5

Descriptive statistics

Standard deviation	69349.38743
Coefficient of variation (CV)	1.475106015
Kurtosis	19.78325514
Mean	47013.1548
Median Absolute Deviation (MAD)	19708.5
Skewness	3.087830046
Sum	1410394644
Variance	4809337537
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	2870	9.6%
390	275	0.9%
780	74	0.2%
326	63	0.2%
316	62	0.2%
396	48	0.2%
2500	40	0.1%
2400	39	0.1%
416	29	0.1%
200	27	0.1%
Other values (22016)	26473	88.2%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
-157264	1	< 0.1%
-61506	1	< 0.1%
-46127	1	< 0.1%
-34041	1	< 0.1%
-25443	1	< 0.1%
-24702	1	< 0.1%
-20320	1	< 0.1%
-17706	1	< 0.1%
-15910	1	< 0.1%
-15641	1	< 0.1%

Value	Count	Frequency (%)
1664089	1	< 0.1%
855086	1	< 0.1%
693131	1	< 0.1%
689643	1	< 0.1%
689627	1	< 0.1%
632041	1	< 0.1%
597415	1	< 0.1%
578971	1	< 0.1%
577957	1	< 0.1%
577015	1	< 0.1%

BIL_AMT_JUN
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	21548
Distinct (%)	71.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	43262.94897

Minimum	-170000
Maximum	891586
Zeros	3195
Zeros (%)	10.7%
Negative	675
Negative (%)	2.2%
Memory size	234.5 KiB

Quantile statistics

Minimum	-170000
5-th percentile	0
Q1	2326.75
median	19052
Q3	54506
95-th percentile	174333.35
Maximum	891586
Range	1061586
Interquartile range (IQR)	52179.25

Descriptive statistics

Standard deviation	64332.85613
Coefficient of variation (CV)	1.487019671
Kurtosis	11.30932483
Mean	43262.94897
Median Absolute Deviation (MAD)	18656
Skewness	2.821965291
Sum	1297888469
Variance	4138716378
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	3195	10.7%
390	246	0.8%
780	101	0.3%
316	68	0.2%
326	62	0.2%
396	44	0.1%
2400	39	0.1%
150	39	0.1%
2500	34	0.1%
416	33	0.1%
Other values (21538)	26139	87.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
-170000	1	< 0.1%
-81334	1	< 0.1%
-65167	1	< 0.1%
-50616	1	< 0.1%
-46627	1	< 0.1%
-34503	1	< 0.1%
-27490	1	< 0.1%
-24303	1	< 0.1%
-22108	1	< 0.1%
-20320	1	< 0.1%

Value	Count	Frequency (%)
891586	1	< 0.1%
706864	1	< 0.1%
628699	1	< 0.1%
616836	1	< 0.1%
572805	1	< 0.1%
569034	1	< 0.1%
565669	1	< 0.1%
563543	1	< 0.1%
548020	1	< 0.1%
542653	1	< 0.1%

BIL_AMT_MAY
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	21010
Distinct (%)	70.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	40311.40097

Minimum	-81334
Maximum	927171
Zeros	3506
Zeros (%)	11.7%
Negative	655
Negative (%)	2.2%
Memory size	234.5 KiB

Quantile statistics

Minimum	-81334
5-th percentile	0
Q1	1763
median	18104.5
Q3	50190.5
95-th percentile	165794.3
Maximum	927171
Range	1008505
Interquartile range (IQR)	48427.5

Descriptive statistics

Standard deviation	60797.15577
Coefficient of variation (CV)	1.508187617
Kurtosis	12.30588129
Mean	40311.40097
Median Absolute Deviation (MAD)	17688.5
Skewness	2.876379867
Sum	1209342029
Variance	3696294150
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	3506	11.7%
390	235	0.8%
780	94	0.3%
316	79	0.3%
326	62	0.2%
150	58	0.2%
396	47	0.2%
2400	39	0.1%
2500	37	0.1%
416	36	0.1%
Other values (21000)	25807	86.0%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
-81334	1	< 0.1%
-61372	1	< 0.1%
-53007	1	< 0.1%
-46627	1	< 0.1%
-37594	1	< 0.1%
-36156	1	< 0.1%
-30481	1	< 0.1%
-28335	1	< 0.1%
-23003	1	< 0.1%
-20753	1	< 0.1%

Value	Count	Frequency (%)
927171	1	< 0.1%
823540	1	< 0.1%
587067	1	< 0.1%
551702	1	< 0.1%
547880	1	< 0.1%
530672	1	< 0.1%
524315	1	< 0.1%
516139	1	< 0.1%
514114	1	< 0.1%
508213	1	< 0.1%

BIL_AMT_APR
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	20604
Distinct (%)	68.7%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	38871.7604

Minimum	-339603
Maximum	961664
Zeros	4020
Zeros (%)	13.4%
Negative	688
Negative (%)	2.3%
Memory size	234.5 KiB

Quantile statistics

Minimum	-339603
5-th percentile	0
Q1	1256
median	17071
Q3	49198.25
95-th percentile	161912
Maximum	961664
Range	1301267
Interquartile range (IQR)	47942.25

Descriptive statistics

Standard deviation	59554.10754
Coefficient of variation (CV)	1.53206613
Kurtosis	12.27070529
Mean	38871.7604
Median Absolute Deviation (MAD)	16755
Skewness	2.846644576
Sum	1166152812
Variance	3546691724
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	4020	13.4%
390	207	0.7%
780	86	0.3%
150	78	0.3%
316	77	0.3%
326	56	0.2%
396	45	0.1%
416	36	0.1%
-18	33	0.1%
2400	32	0.1%
Other values (20594)	25330	84.4%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
-339603	1	< 0.1%
-209051	1	< 0.1%
-150953	1	< 0.1%
-94625	1	< 0.1%
-73895	1	< 0.1%
-57060	1	< 0.1%
-51443	1	< 0.1%
-51183	1	< 0.1%
-46627	1	< 0.1%
-45734	1	< 0.1%

Value	Count	Frequency (%)
961664	1	< 0.1%
699944	1	< 0.1%
568638	1	< 0.1%
527711	1	< 0.1%
527566	1	< 0.1%
514975	1	< 0.1%
513798	1	< 0.1%
511905	1	< 0.1%
501370	1	< 0.1%
499100	1	< 0.1%

PAY_AMT_SEP
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	7943
Distinct (%)	26.5%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	5663.5805

Minimum	0
Maximum	873552
Zeros	5249
Zeros (%)	17.5%
Negative	0
Negative (%)	0.0%
Memory size	234.5 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	1000
median	2100
Q3	5006
95-th percentile	18428.2
Maximum	873552
Range	873552
Interquartile range (IQR)	4006

Descriptive statistics

Standard deviation	16563.28035
Coefficient of variation (CV)	2.924524575
Kurtosis	415.2547427
Mean	5663.5805
Median Absolute Deviation (MAD)	1932
Skewness	14.66836433
Sum	169907415
Variance	274342256.1
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	5249	17.5%
2000	1363	4.5%
3000	891	3.0%
5000	698	2.3%
1500	507	1.7%
4000	426	1.4%
10000	401	1.3%
1000	365	1.2%
2500	298	1.0%
6000	294	1.0%
Other values (7933)	19508	65.0%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	5249	17.5%
1	9	< 0.1%
2	14	< 0.1%
3	15	0.1%
4	18	0.1%
5	12	< 0.1%
6	15	0.1%
7	9	< 0.1%
8	8	< 0.1%
9	7	< 0.1%

Value	Count	Frequency (%)
873552	1	< 0.1%
505000	1	< 0.1%
493358	1	< 0.1%
423903	1	< 0.1%
405016	1	< 0.1%
368199	1	< 0.1%
323014	1	< 0.1%
304815	1	< 0.1%
302000	1	< 0.1%
300039	1	< 0.1%

PAY_AMT_AUG
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct	7899
Distinct (%)	26.3%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	5921.1635

Minimum	0
Maximum	1684259
Zeros	5396
Zeros (%)	18.0%
Negative	0
Negative (%)	0.0%
Memory size	234.5 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	833
median	2009
Q3	5000
95-th percentile	19004.35
Maximum	1684259
Range	1684259
Interquartile range (IQR)	4167

Descriptive statistics

Standard deviation	23040.8704
Coefficient of variation (CV)	3.891274139
Kurtosis	1641.631911
Mean	5921.1635
Median Absolute Deviation (MAD)	1991
Skewness	30.45381745
Sum	177634905
Variance	530881708.9
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	5396	18.0%
2000	1290	4.3%
3000	857	2.9%
5000	717	2.4%
1000	594	2.0%
1500	521	1.7%
4000	410	1.4%
10000	318	1.1%
6000	283	0.9%
2500	251	0.8%
Other values (7889)	19363	64.5%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	5396	18.0%
1	15	0.1%
2	20	0.1%
3	18	0.1%
4	11	< 0.1%
5	25	0.1%
6	8	< 0.1%
7	12	< 0.1%
8	9	< 0.1%
9	6	< 0.1%

Value	Count	Frequency (%)
1684259	1	< 0.1%
1227082	1	< 0.1%
1215471	1	< 0.1%
1024516	1	< 0.1%
580464	1	< 0.1%
415552	1	< 0.1%
401003	1	< 0.1%
388126	1	< 0.1%
385228	1	< 0.1%
384986	1	< 0.1%

PAY_AMT_JUL
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	7518
Distinct (%)	25.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	5225.6815

Minimum	0
Maximum	896040
Zeros	5968
Zeros (%)	19.9%
Negative	0
Negative (%)	0.0%
Memory size	234.5 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	390
median	1800
Q3	4505
95-th percentile	17589.4
Maximum	896040
Range	896040
Interquartile range (IQR)	4115

Descriptive statistics

Standard deviation	17606.96147
Coefficient of variation (CV)	3.36931393
Kurtosis	564.3112295
Mean	5225.6815
Median Absolute Deviation (MAD)	1795
Skewness	17.21663544
Sum	156770445
Variance	310005092.2
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	5968	19.9%
2000	1285	4.3%
1000	1103	3.7%
3000	870	2.9%
5000	721	2.4%
1500	490	1.6%
4000	381	1.3%
10000	312	1.0%
1200	243	0.8%
6000	241	0.8%
Other values (7508)	18386	61.3%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	5968	19.9%
1	13	< 0.1%
2	19	0.1%
3	14	< 0.1%
4	15	0.1%
5	18	0.1%
6	14	< 0.1%
7	18	0.1%
8	10	< 0.1%
9	12	< 0.1%

Value	Count	Frequency (%)
896040	1	< 0.1%
889043	1	< 0.1%
508229	1	< 0.1%
417588	1	< 0.1%
400972	1	< 0.1%
397092	1	< 0.1%
380478	1	< 0.1%
371718	1	< 0.1%
349395	1	< 0.1%
344261	1	< 0.1%

PAY_AMT_JUN
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	6937
Distinct (%)	23.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	4826.076867

Minimum	0
Maximum	621000
Zeros	6408
Zeros (%)	21.4%
Negative	0
Negative (%)	0.0%
Memory size	234.5 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	296
median	1500
Q3	4013.25
95-th percentile	16014.95
Maximum	621000
Range	621000
Interquartile range (IQR)	3717.25

Descriptive statistics

Standard deviation	15666.15974
Coefficient of variation (CV)	3.246147995
Kurtosis	277.3337677
Mean	4826.076867
Median Absolute Deviation (MAD)	1500
Skewness	12.90498482
Sum	144782306
Variance	245428561.1
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	6408	21.4%
1000	1394	4.6%
2000	1214	4.0%
3000	887	3.0%
5000	810	2.7%
1500	441	1.5%
4000	402	1.3%
10000	341	1.1%
2500	259	0.9%
500	258	0.9%
Other values (6927)	17586	58.6%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	6408	21.4%
1	22	0.1%
2	22	0.1%
3	13	< 0.1%
4	20	0.1%
5	12	< 0.1%
6	16	0.1%
7	11	< 0.1%
8	7	< 0.1%
9	9	< 0.1%

Value	Count	Frequency (%)
621000	1	< 0.1%
528897	1	< 0.1%
497000	1	< 0.1%
432130	1	< 0.1%
400046	1	< 0.1%
331788	1	< 0.1%
330982	1	< 0.1%
320008	1	< 0.1%
313094	1	< 0.1%
292962	1	< 0.1%

PAY_AMT_MAY
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	6897
Distinct (%)	23.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	4799.387633

Minimum	0
Maximum	426529
Zeros	6703
Zeros (%)	22.3%
Negative	0
Negative (%)	0.0%
Memory size	234.5 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	252.5
median	1500
Q3	4031.5
95-th percentile	16000
Maximum	426529
Range	426529
Interquartile range (IQR)	3779

Descriptive statistics

Standard deviation	15278.30568
Coefficient of variation (CV)	3.183386475
Kurtosis	180.0639402
Mean	4799.387633
Median Absolute Deviation (MAD)	1500
Skewness	11.12741705
Sum	143981629
Variance	233426624.4
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	6703	22.3%
1000	1340	4.5%
2000	1323	4.4%
3000	947	3.2%
5000	814	2.7%
1500	426	1.4%
4000	401	1.3%
10000	343	1.1%
500	250	0.8%
6000	247	0.8%
Other values (6887)	17206	57.4%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	6703	22.3%
1	21	0.1%
2	13	< 0.1%
3	13	< 0.1%
4	12	< 0.1%
5	9	< 0.1%
6	7	< 0.1%
7	9	< 0.1%
8	6	< 0.1%
9	6	< 0.1%

Value	Count	Frequency (%)
426529	1	< 0.1%
417990	1	< 0.1%
388071	1	< 0.1%
379267	1	< 0.1%
332000	1	< 0.1%
331788	1	< 0.1%
330982	1	< 0.1%
326889	1	< 0.1%
317077	1	< 0.1%
310135	1	< 0.1%

PAY_AMT_APR
Real number (ℝ_≥0)

HIGH CORRELATION
ZEROS

Distinct	6939
Distinct (%)	23.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	5215.502567

Minimum	0
Maximum	528666
Zeros	7173
Zeros (%)	23.9%
Negative	0
Negative (%)	0.0%
Memory size	234.5 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	117.75
median	1500
Q3	4000
95-th percentile	17343.8
Maximum	528666
Range	528666
Interquartile range (IQR)	3882.25

Descriptive statistics

Standard deviation	17777.46578
Coefficient of variation (CV)	3.408581541
Kurtosis	167.1614296
Mean	5215.502567
Median Absolute Deviation (MAD)	1500
Skewness	10.64072733
Sum	156465077
Variance	316038289.4
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	7173	23.9%
1000	1299	4.3%
2000	1295	4.3%
3000	914	3.0%
5000	808	2.7%
1500	439	1.5%
4000	411	1.4%
10000	356	1.2%
500	247	0.8%
6000	220	0.7%
Other values (6929)	16838	56.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	7173	23.9%
1	20	0.1%
2	9	< 0.1%
3	14	< 0.1%
4	12	< 0.1%
5	7	< 0.1%
6	6	< 0.1%
7	5	< 0.1%
8	6	< 0.1%
9	7	< 0.1%

Value	Count	Frequency (%)
528666	1	< 0.1%
527143	1	< 0.1%
443001	1	< 0.1%
422000	1	< 0.1%
403500	1	< 0.1%
377000	1	< 0.1%
372495	1	< 0.1%
351282	1	< 0.1%
345293	1	< 0.1%
308000	1	< 0.1%

DEFAULT
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	234.5 KiB

not default	23364
default	6636

Length

Max length	11
Median length	11
Mean length	10.1152
Min length	7

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	default
2nd row	default
3rd row	not default
4th row	not default
5th row	not default

Common Values

Value	Count	Frequency (%)
not default	23364	77.9%
default	6636	22.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
default	30000	56.2%
not	23364	43.8%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

SEX_V
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	234.5 KiB

female	18112
male	11888

Length

Max length	6
Median length	6
Mean length	5.207466667
Min length	4

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	female
2nd row	female
3rd row	female
4th row	female
5th row	male

Common Values

Value	Count	Frequency (%)
female	18112	60.4%
male	11888	39.6%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
female	18112	60.4%
male	11888	39.6%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

EDUCATION_V
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	4
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	234.5 KiB

university	14030
graduate school	10585
high school	4917
other	468

Length

Max length	15
Median length	11
Mean length	11.85006667
Min length	5

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	university
2nd row	university
3rd row	university
4th row	university
5th row	university

Common Values

Value	Count	Frequency (%)
university	14030	46.8%
graduate school	10585	35.3%
high school	4917	16.4%
other	468	1.6%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
school	15502	34.1%
university	14030	30.8%
graduate	10585	23.3%
high	4917	10.8%
other	468	1.0%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

MARRIAGE_V
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	4
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	234.5 KiB

single	15964
married	13659
divorce	323
others	54

Length

Max length	7
Median length	6
Mean length	6.466066667
Min length	6

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	married
2nd row	single
3rd row	single
4th row	married
5th row	married

Common Values

Value	Count	Frequency (%)
single	15964	53.2%
married	13659	45.5%
divorce	323	1.1%
others	54	0.2%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
single	15964	53.2%
married	13659	45.5%
divorce	323	1.1%
others	54	0.2%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

SEX_female
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	234.5 KiB

1	18112
0	11888

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	1
3rd row	1
4th row	1
5th row	0

Common Values

Value	Count	Frequency (%)
1	18112	60.4%
0	11888	39.6%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
1	18112	60.4%
0	11888	39.6%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

SEX_male
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	234.5 KiB

0	18112
1	11888

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	1

Common Values

Value	Count	Frequency (%)
0	18112	60.4%
1	11888	39.6%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	18112	60.4%
1	11888	39.6%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

EDUCATION_graduate school
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	234.5 KiB

0	19415
1	10585

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	19415	64.7%
1	10585	35.3%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	19415	64.7%
1	10585	35.3%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

EDUCATION_high school
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	234.5 KiB

0	25083
1	4917

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	25083	83.6%
1	4917	16.4%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	25083	83.6%
1	4917	16.4%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

EDUCATION_other
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	234.5 KiB

0	29532
1	468

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	29532	98.4%
1	468	1.6%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	29532	98.4%
1	468	1.6%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

EDUCATION_university
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	234.5 KiB

0	15970
1	14030

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	1
3rd row	1
4th row	1
5th row	1

Common Values

Value	Count	Frequency (%)
0	15970	53.2%
1	14030	46.8%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	15970	53.2%
1	14030	46.8%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

MARRIAGE_divorce
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	234.5 KiB

0	29677
1	323

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	29677	98.9%
1	323	1.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	29677	98.9%
1	323	1.1%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

MARRIAGE_married
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	234.5 KiB

0	16341
1	13659

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	0
3rd row	0
4th row	1
5th row	1

Common Values

Value	Count	Frequency (%)
0	16341	54.5%
1	13659	45.5%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	16341	54.5%
1	13659	45.5%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

MARRIAGE_others
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	234.5 KiB

0	29946
1	54

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	29946	99.8%
1	54	0.2%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	29946	99.8%
1	54	0.2%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

MARRIAGE_single
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	234.5 KiB

1	15964
0	14036

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	1
3rd row	1
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
1	15964	53.2%
0	14036	46.8%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
1	15964	53.2%
0	14036	46.8%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

DEFAULT_NUM
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	234.5 KiB

0	23364
1	6636

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	1
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	23364	77.9%
1	6636	22.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	23364	77.9%
1	6636	22.1%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

NO_CONS_APR
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	234.5 KiB

0	25105
1	4895

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	25105	83.7%
1	4895	16.3%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	25105	83.7%
1	4895	16.3%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

PAID_FULL_APR
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	234.5 KiB

0	24260
1	5740

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	24260	80.9%
1	5740	19.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	24260	80.9%
1	5740	19.1%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

REVOLVING_USE_APR
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	234.5 KiB

1	16286
0	13714

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	1
4th row	1
5th row	1

Common Values

Value	Count	Frequency (%)
1	16286	54.3%
0	13714	45.7%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
1	16286	54.3%
0	13714	45.7%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

NO_CONS_MAY
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	234.5 KiB

0	25454
1	4546

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	25454	84.8%
1	4546	15.2%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	25454	84.8%
1	4546	15.2%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

PAID_FULL_MAY
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	234.5 KiB

0	24461
1	5539

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	24461	81.5%
1	5539	18.5%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	24461	81.5%
1	5539	18.5%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

REVOLVING_USE_MAY
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	234.5 KiB

1	16947
0	13053

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	1
3rd row	1
4th row	1
5th row	1

Common Values

Value	Count	Frequency (%)
1	16947	56.5%
0	13053	43.5%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
1	16947	56.5%
0	13053	43.5%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

NO_CONS_JUN
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	234.5 KiB

0	25652
1	4348

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	25652	85.5%
1	4348	14.5%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	25652	85.5%
1	4348	14.5%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

PAID_FULL_JUN
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	234.5 KiB

0	24313
1	5687

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	24313	81.0%
1	5687	19.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	24313	81.0%
1	5687	19.0%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

REVOLVING_USE_JUN
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	234.5 KiB

1	16455
0	13545

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	1
3rd row	1
4th row	1
5th row	1

Common Values

Value	Count	Frequency (%)
1	16455	54.9%
0	13545	45.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
1	16455	54.9%
0	13545	45.1%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

NO_CONS_JUL
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	234.5 KiB

0	25915
1	4085

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	25915	86.4%
1	4085	13.6%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	25915	86.4%
1	4085	13.6%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

PAID_FULL_JUL
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	234.5 KiB

0	24062
1	5938

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	0
3rd row	0
4th row	0
5th row	1

Common Values

Value	Count	Frequency (%)
0	24062	80.2%
1	5938	19.8%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	24062	80.2%
1	5938	19.8%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

REVOLVING_USE_JUL
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	234.5 KiB

1	15764
0	14236

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	1
3rd row	1
4th row	1
5th row	0

Common Values

Value	Count	Frequency (%)
1	15764	52.5%
0	14236	47.5%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
1	15764	52.5%
0	14236	47.5%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

NO_CONS_AUG
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	234.5 KiB

0	26218
1	3782

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	26218	87.4%
1	3782	12.6%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	26218	87.4%
1	3782	12.6%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

PAID_FULL_AUG
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	234.5 KiB

0	23950
1	6050

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	23950	79.8%
1	6050	20.2%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	23950	79.8%
1	6050	20.2%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

REVOLVING_USE_AUG
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	234.5 KiB

1	15730
0	14270

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	1
4th row	1
5th row	1

Common Values

Value	Count	Frequency (%)
1	15730	52.4%
0	14270	47.6%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
1	15730	52.4%
0	14270	47.6%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

NO_CONS_SEP
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	234.5 KiB

0	27241
1	2759

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	27241	90.8%
1	2759	9.2%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	27241	90.8%
1	2759	9.2%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

PAID_FULL_SEP
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	234.5 KiB

0	24314
1	5686

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	1
3rd row	0
4th row	0
5th row	1

Common Values

Value	Count	Frequency (%)
0	24314	81.0%
1	5686	19.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	24314	81.0%
1	5686	19.0%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

REVOLVING_USE_SEP
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	234.5 KiB

0	15263
1	14737

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	1
4th row	1
5th row	0

Common Values

Value	Count	Frequency (%)
0	15263	50.9%
1	14737	49.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	15263	50.9%
1	14737	49.1%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

PAY_DELAY_APR
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	8
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.2265666667

Minimum	0
Maximum	8
Zeros	26921
Zeros (%)	89.7%
Negative	0
Negative (%)	0.0%
Memory size	234.5 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	2
Maximum	8
Range	8
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	0.7154378198
Coefficient of variation (CV)	3.157736441
Kurtosis	19.93708244
Mean	0.2265666667
Median Absolute Deviation (MAD)	0
Skewness	3.821385108
Sum	6797
Variance	0.5118512739
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=8)

Value	Count	Frequency (%)
0	26921	89.7%
2	2766	9.2%
3	184	0.6%
4	49	0.2%
7	46	0.2%
6	19	0.1%
5	13	< 0.1%
8	2	< 0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	26921	89.7%
2	2766	9.2%
3	184	0.6%
4	49	0.2%
5	13	< 0.1%
6	19	0.1%
7	46	0.2%
8	2	< 0.1%

Value	Count	Frequency (%)
8	2	< 0.1%
7	46	0.2%
6	19	0.1%
5	13	< 0.1%
4	49	0.2%
3	184	0.6%
2	2766	9.2%
0	26921	89.7%

PAY_DELAY_MAY
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	8
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.2215

Minimum	0
Maximum	8
Zeros	27032
Zeros (%)	90.1%
Negative	0
Negative (%)	0.0%
Memory size	234.5 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	2
Maximum	8
Range	8
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	0.7177197137
Coefficient of variation (CV)	3.240269588
Kurtosis	21.31341111
Mean	0.2215
Median Absolute Deviation (MAD)	0
Skewness	3.966570819
Sum	6645
Variance	0.5151215874
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=8)

Value	Count	Frequency (%)
0	27032	90.1%
2	2626	8.8%
3	178	0.6%
4	84	0.3%
7	58	0.2%
5	17	0.1%
6	4	< 0.1%
8	1	< 0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	27032	90.1%
2	2626	8.8%
3	178	0.6%
4	84	0.3%
5	17	0.1%
6	4	< 0.1%
7	58	0.2%
8	1	< 0.1%

Value	Count	Frequency (%)
8	1	< 0.1%
7	58	0.2%
6	4	< 0.1%
5	17	0.1%
4	84	0.3%
3	178	0.6%
2	2626	8.8%
0	27032	90.1%

PAY_DELAY_JUN
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	9
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.2587666667

Minimum	0
Maximum	8
Zeros	26490
Zeros (%)	88.3%
Negative	0
Negative (%)	0.0%
Memory size	234.5 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	2
Maximum	8
Range	8
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	0.761112643
Coefficient of variation (CV)	2.941308681
Kurtosis	17.16067039
Mean	0.2587666667
Median Absolute Deviation (MAD)	0
Skewness	3.547022193
Sum	7763
Variance	0.5792924553
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=9)

Value	Count	Frequency (%)
0	26490	88.3%
2	3159	10.5%
3	180	0.6%
4	69	0.2%
7	58	0.2%
5	35	0.1%
6	5	< 0.1%
1	2	< 0.1%
8	2	< 0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	26490	88.3%
1	2	< 0.1%
2	3159	10.5%
3	180	0.6%
4	69	0.2%
5	35	0.1%
6	5	< 0.1%
7	58	0.2%
8	2	< 0.1%

Value	Count	Frequency (%)
8	2	< 0.1%
7	58	0.2%
6	5	< 0.1%
5	35	0.1%
4	69	0.2%
3	180	0.6%
2	3159	10.5%
1	2	< 0.1%
0	26490	88.3%

PAY_DELAY_JUL
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	9
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.3040666667

Minimum	0
Maximum	8
Zeros	25787
Zeros (%)	86.0%
Negative	0
Negative (%)	0.0%
Memory size	234.5 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	2
Maximum	8
Range	8
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	0.7905889977
Coefficient of variation (CV)	2.600051516
Kurtosis	10.47514326
Mean	0.3040666667
Median Absolute Deviation (MAD)	0
Skewness	2.85658557
Sum	9122
Variance	0.6250309633
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=9)

Value	Count	Frequency (%)
0	25787	86.0%
2	3819	12.7%
3	240	0.8%
4	76	0.3%
7	27	0.1%
6	23	0.1%
5	21	0.1%
1	4	< 0.1%
8	3	< 0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	25787	86.0%
1	4	< 0.1%
2	3819	12.7%
3	240	0.8%
4	76	0.3%
5	21	0.1%
6	23	0.1%
7	27	0.1%
8	3	< 0.1%

Value	Count	Frequency (%)
8	3	< 0.1%
7	27	0.1%
6	23	0.1%
5	21	0.1%
4	76	0.3%
3	240	0.8%
2	3819	12.7%
1	4	< 0.1%
0	25787	86.0%

PAY_DELAY_AUG
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	9
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.3200333333

Minimum	0
Maximum	8
Zeros	25562
Zeros (%)	85.2%
Negative	0
Negative (%)	0.0%
Memory size	234.5 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	2
Maximum	8
Range	8
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	0.8017273588
Coefficient of variation (CV)	2.505137044
Kurtosis	7.83849624
Mean	0.3200333333
Median Absolute Deviation (MAD)	0
Skewness	2.598928087
Sum	9601
Variance	0.6427667578
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=9)

Value	Count	Frequency (%)
0	25562	85.2%
2	3927	13.1%
3	326	1.1%
4	99	0.3%
1	28	0.1%
5	25	0.1%
7	20	0.1%
6	12	< 0.1%
8	1	< 0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	25562	85.2%
1	28	0.1%
2	3927	13.1%
3	326	1.1%
4	99	0.3%
5	25	0.1%
6	12	< 0.1%
7	20	0.1%
8	1	< 0.1%

Value	Count	Frequency (%)
8	1	< 0.1%
7	20	0.1%
6	12	< 0.1%
5	25	0.1%
4	99	0.3%
3	326	1.1%
2	3927	13.1%
1	28	0.1%
0	25562	85.2%

PAY_DELAY_SEP
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	9
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.3567666667

Minimum	0
Maximum	8
Zeros	23182
Zeros (%)	77.3%
Negative	0
Negative (%)	0.0%
Memory size	234.5 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	2
Maximum	8
Range	8
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	0.7605941728
Coefficient of variation (CV)	2.131909295
Kurtosis	12.45880323
Mean	0.3567666667
Median Absolute Deviation (MAD)	0
Skewness	2.809796725
Sum	10703
Variance	0.5785034957
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=9)

Value	Count	Frequency (%)
0	23182	77.3%
1	3688	12.3%
2	2667	8.9%
3	322	1.1%
4	76	0.3%
5	26	0.1%
8	19	0.1%
6	11	< 0.1%
7	9	< 0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	23182	77.3%
1	3688	12.3%
2	2667	8.9%
3	322	1.1%
4	76	0.3%
5	26	0.1%
6	11	< 0.1%
7	9	< 0.1%
8	19	0.1%

Value	Count	Frequency (%)
8	19	0.1%
7	9	< 0.1%
6	11	< 0.1%
5	26	0.1%
4	76	0.3%
3	322	1.1%
2	2667	8.9%
1	3688	12.3%
0	23182	77.3%

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Count
Matrix

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

First rows

	LIMIT_BAL	AGE	PAY_SEP	PAY_AUG	PAY_JUL	PAY_JUN	PAY_MAY	PAY_APR	BIL_AMT_SEP	BIL_AMT_AUG	BIL_AMT_JUL	BIL_AMT_JUN	BIL_AMT_MAY	BIL_AMT_APR	PAY_AMT_SEP	PAY_AMT_AUG	PAY_AMT_JUL	PAY_AMT_JUN	PAY_AMT_MAY	PAY_AMT_APR	DEFAULT	SEX_V	EDUCATION_V	MARRIAGE_V	SEX_female	SEX_male	EDUCATION_graduate school	EDUCATION_high school	EDUCATION_university	MARRIAGE_married	MARRIAGE_single	DEFAULT_NUM	NO_CONS_APR	PAID_FULL_APR	REVOLVING_USE_APR	NO_CONS_MAY	PAID_FULL_MAY	REVOLVING_USE_MAY	NO_CONS_JUN	PAID_FULL_JUN	REVOLVING_USE_JUN	NO_CONS_JUL	PAID_FULL_JUL	REVOLVING_USE_JUL	NO_CONS_AUG	PAID_FULL_AUG	REVOLVING_USE_AUG	NO_CONS_SEP	PAID_FULL_SEP	REVOLVING_USE_SEP	PAY_DELAY_APR	PAY_DELAY_JUL	PAY_DELAY_AUG	PAY_DELAY_SEP
0	20000	24	2	2	-1	-1	-2	-2	3913	3102	689	0	0	0	0	689	0	0	0	0	default	female	university	married	1	0	0	0	1	1	0	1	1	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	0	0	2	2
1	120000	26	-1	2	0	0	0	2	2682	1725	2682	3272	3455	3261	0	1000	1000	1000	0	2000	default	female	university	single	1	0	0	0	1	0	1	1	0	0	0	0	0	1	0	0	1	0	0	1	0	0	0	0	1	0	2	0	2	0
2	90000	34	0	0	0	0	0	0	29239	14027	13559	14331	14948	15549	1518	1500	1000	1000	1000	5000	not default	female	university	single	1	0	0	0	1	0	1	0	0	0	1	0	0	1	0	0	1	0	0	1	0	0	1	0	0	1	0	0	0	0
3	50000	37	0	0	0	0	0	0	46990	48233	49291	28314	28959	29547	2000	2019	1200	1100	1069	1000	not default	female	university	married	1	0	0	0	1	1	0	0	0	0	1	0	0	1	0	0	1	0	0	1	0	0	1	0	0	1	0	0	0	0
4	50000	57	-1	0	-1	0	0	0	8617	5670	35835	20940	19146	19131	2000	36681	10000	9000	689	679	not default	male	university	married	0	1	0	0	1	1	0	0	0	0	1	0	0	1	0	0	1	0	1	0	0	0	1	0	1	0	0	0	0	0
5	50000	37	0	0	0	0	0	0	64400	57069	57608	19394	19619	20024	2500	1815	657	1000	1000	800	not default	male	graduate school	single	0	1	1	0	0	0	1	0	0	0	1	0	0	1	0	0	1	0	0	1	0	0	1	0	0	1	0	0	0	0
6	500000	29	0	0	0	0	0	0	367965	412023	445007	542653	483003	473944	55000	40000	38000	20239	13750	13770	not default	male	graduate school	single	0	1	1	0	0	0	1	0	0	0	1	0	0	1	0	0	1	0	0	1	0	0	1	0	0	1	0	0	0	0
7	100000	23	0	-1	-1	0	0	-1	11876	380	601	221	-159	567	380	601	0	581	1687	1542	not default	female	university	single	1	0	0	0	1	0	1	0	0	1	0	0	0	1	0	0	1	0	1	0	0	1	0	0	0	1	0	0	0	0
8	140000	28	0	0	2	0	0	0	11285	14096	12108	12211	11793	3719	3329	0	432	1000	1000	1000	not default	female	high school	married	1	0	0	1	0	1	0	0	0	0	1	0	0	1	0	0	1	0	0	0	0	0	1	0	0	1	0	2	0	0
9	20000	35	-2	-2	-2	-2	-1	-1	0	0	0	0	13007	13912	0	0	0	13007	1122	0	not default	male	high school	single	0	1	0	1	0	0	1	0	0	1	0	0	1	0	1	0	0	1	0	0	1	0	0	1	0	0	0	0	0	0

Last rows

	LIMIT_BAL	AGE	PAY_SEP	PAY_AUG	PAY_JUL	PAY_JUN	PAY_MAY	PAY_APR	BIL_AMT_SEP	BIL_AMT_AUG	BIL_AMT_JUL	BIL_AMT_JUN	BIL_AMT_MAY	BIL_AMT_APR	PAY_AMT_SEP	PAY_AMT_AUG	PAY_AMT_JUL	PAY_AMT_JUN	PAY_AMT_MAY	PAY_AMT_APR	DEFAULT	SEX_V	EDUCATION_V	MARRIAGE_V	SEX_male	EDUCATION_graduate school	EDUCATION_high school	EDUCATION_university	MARRIAGE_married	MARRIAGE_single	DEFAULT_NUM	NO_CONS_APR	PAID_FULL_APR	REVOLVING_USE_APR	NO_CONS_MAY	REVOLVING_USE_MAY	NO_CONS_JUN	PAID_FULL_JUN	REVOLVING_USE_JUN	PAID_FULL_JUL	REVOLVING_USE_JUL	PAID_FULL_AUG	REVOLVING_USE_AUG	PAID_FULL_SEP	REVOLVING_USE_SEP	PAY_DELAY_APR	PAY_DELAY_MAY	PAY_DELAY_JUN	PAY_DELAY_JUL	PAY_DELAY_AUG	PAY_DELAY_SEP
29990	140000	41	0	0	0	0	0	0	138325	137142	139110	138262	49675	46121	6000	7000	4228	1505	2000	2000	not default	male	university	married	1	0	0	1	1	0	0	0	0	1	0	1	0	0	1	0	1	0	1	0	1	0	0	0	0	0	0
29991	210000	34	3	2	2	2	2	2	2500	2500	2500	2500	2500	2500	0	0	0	0	0	0	default	male	university	married	1	0	0	1	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	2	2	2	2	2	3
29992	10000	43	0	0	0	-2	-2	-2	8802	10400	0	0	0	0	2000	0	0	0	0	0	not default	male	high school	married	1	0	1	0	1	0	0	1	0	0	1	0	1	0	0	0	1	0	1	0	1	0	0	0	0	0	0
29993	100000	38	0	-1	-1	0	0	0	3042	1427	102996	70626	69473	55004	2000	111784	4000	3000	2000	2000	not default	male	graduate school	single	1	1	0	0	0	1	0	0	0	1	0	1	0	0	1	1	0	1	0	0	1	0	0	0	0	0	0
29994	80000	34	2	2	2	2	2	2	72557	77708	79384	77519	82607	81158	7000	3500	0	7000	0	4000	default	male	university	single	1	0	0	1	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	2	2	2	2	2	2
29995	220000	39	0	0	0	0	0	0	188948	192815	208365	88004	31237	15980	8500	20000	5003	3047	5000	1000	not default	male	high school	married	1	0	1	0	1	0	0	0	0	1	0	1	0	0	1	0	1	0	1	0	1	0	0	0	0	0	0
29996	150000	43	-1	-1	-1	-1	0	0	1683	1828	3502	8979	5190	0	1837	3526	8998	129	0	0	not default	male	high school	single	1	0	1	0	0	1	0	0	0	1	0	1	0	1	0	1	0	1	0	1	0	0	0	0	0	0	0
29997	30000	37	4	3	2	-1	0	0	3565	3356	2758	20878	20582	19357	0	0	22000	4200	2000	3100	default	male	university	single	1	0	0	1	0	1	1	0	0	1	0	1	0	1	0	0	0	0	0	0	0	0	0	0	2	3	4
29998	80000	41	1	-1	0	0	0	-1	-1645	78379	76304	52774	11855	48944	85900	3409	1178	1926	52964	1804	default	male	high school	married	1	0	1	0	1	0	1	0	1	0	0	1	0	0	1	0	1	1	0	0	0	0	0	0	0	0	1
29999	50000	46	0	0	0	0	0	0	47929	48905	49764	36535	32428	15313	2078	1800	1430	1000	1000	1000	default	male	university	married	1	0	0	1	1	0	1	0	0	1	0	1	0	0	1	0	1	0	1	0	1	0	0	0	0	0	0

Most frequently occurring

	LIMIT_BAL	AGE	PAY_SEP	PAY_AUG	PAY_JUL	PAY_JUN	PAY_MAY	PAY_APR	BIL_AMT_SEP	BIL_AMT_AUG	BIL_AMT_JUL	BIL_AMT_JUN	BIL_AMT_MAY	BIL_AMT_APR	DEFAULT	SEX_V	EDUCATION_V	MARRIAGE_V	SEX_female	SEX_male	EDUCATION_graduate school	EDUCATION_high school	EDUCATION_university	MARRIAGE_married	MARRIAGE_single	DEFAULT_NUM	NO_CONS_APR	NO_CONS_MAY	NO_CONS_JUN	NO_CONS_JUL	NO_CONS_AUG	NO_CONS_SEP	PAY_DELAY_APR	PAY_DELAY_MAY	PAY_DELAY_JUN	PAY_DELAY_JUL	PAY_DELAY_AUG	PAY_DELAY_SEP	# duplicates
0	20000	24	2	2	4	4	4	4	1650	1650	1650	1650	1650	1650	default	male	university	single	0	1	0	0	1	0	1	1	0	0	0	0	0	0	4	4	4	4	2	2	2
1	50000	23	1	-2	-2	-2	-2	-2	0	0	0	0	0	0	not default	female	graduate school	single	1	0	1	0	0	0	1	0	1	1	1	1	1	0	0	0	0	0	0	1	2
2	50000	26	1	-2	-2	-2	-2	-2	0	0	0	0	0	0	not default	male	university	single	0	1	0	0	1	0	1	0	1	1	1	1	1	0	0	0	0	0	0	1	2
3	80000	25	-2	-2	-2	-2	-2	-2	0	0	0	0	0	0	not default	female	university	single	1	0	0	0	1	0	1	0	1	1	1	1	1	1	0	0	0	0	0	0	2
4	80000	31	-2	-2	-2	-2	-2	-2	0	0	0	0	0	0	not default	female	university	married	1	0	0	0	1	1	0	0	1	1	1	1	1	1	0	0	0	0	0	0	2
5	80000	42	-2	-2	-2	-2	-2	-2	0	0	0	0	0	0	not default	female	high school	married	1	0	0	1	0	1	0	0	1	1	1	1	1	1	0	0	0	0	0	0	2
6	90000	31	1	-2	-2	-2	-2	-2	0	0	0	0	0	0	not default	female	graduate school	single	1	0	1	0	0	0	1	0	1	1	1	1	1	0	0	0	0	0	0	1	2
7	100000	49	1	-2	-2	-2	-2	-2	0	0	0	0	0	0	not default	female	university	married	1	0	0	0	1	1	0	0	1	1	1	1	1	0	0	0	0	0	0	1	2
8	110000	31	1	-2	-2	-2	-2	-2	0	0	0	0	0	0	not default	female	graduate school	single	1	0	1	0	0	0	1	0	1	1	1	1	1	0	0	0	0	0	0	1	2
9	140000	29	1	-2	-2	-2	-2	-2	0	0	0	0	0	0	not default	male	graduate school	single	0	1	1	0	0	0	1	0	1	1	1	1	1	0	0	0	0	0	0	1	2

Overview

Variables

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script